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    Electricity demand forecasting 2030 by decomposition analysis of open data

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    CIRED 2019 - 1756.pdf (437.6Kb)
    Paper number
    1756
    Conference name
    CIRED 2019
    Conference date
    3-6 June 2019
    Conference location
    Madrid, Spain
    Peer-reviewed
    Yes
    Metadata
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    Authors
    Räisänen, Otto , LUT University, Finland
    Haakana, Juha, LUT University, Finland
    Haapaniemi, Jouni, LUT University, Finland
    Lassila, Jukka , LUT University, Finland
    Partanen, Jarmo, LUT University, Finland
    Abstract
    The demand of electrical energy in the household sectorfollowed a nearly linear growth trend for a long timemaking demand forecasting relatively simple. However, inthe last decade the growth has stalled due to energyefficiency policies, structural changes in the society andemergence of new technologies. In sparsely populatedareas the population is continually declining which affectselectrical energy consumption and increases averageconductor length per customer. These changes in theoperational environment pose challenges to demandforecasting. Historical data relating to the change factorscould be used to improve demand forecasts. This studyintroduces a method that uses decomposition and timeseriesanalysis of open data to forecast future electricalenergy demand. The method is used to forecast theelectrical energy consumption for the household sector ina group of Finnish municipalities which have a decliningpopulation.
    Publisher
    AIM
    Date
    2019-06-03
    Published in
    • CIRED 2019 Conference
    Permanent link to this record
    https://cired-repository.org/handle/20.500.12455/557
    http://dx.doi.org/10.34890/780
    ISSN
    2032-9644
    ISBN
    978-2-9602415-0-1

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